• Restorative Dentistry and Endodontics
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• v.32 no.3
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• pp.169-179
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• 2007

This study was to compare the microshear bond strength $({\mu}SBS)$ of light- and chemically cured composites to enamel coupled with four 2-step self-etch adhesives and also to evaluate the incompatibility between 2-step self-etch adhesives and chemically cured composite resin. Crown segments of extracted human molars were cut mesiodistally, and a 1 mm thickness of specimen was made. They were assigned to four groups by adhesives used: SE group (Clearfil SE Bond) AdheSE group (AdheSE), Tyrian group (Tyrian SPE/One-Step Plus), and Contax group (Contax) Each adhesive was applied to a cut enamel surface as per the manufacturer's instruction. Light-cured (Filtek Z250) or chemically cured composite (Luxacore Smartmix Dual) was bonded to the enamel of each specimen using a Tygon tube. After storage in distilled water for 24 hours, the bonded specimens were subjected to ${\mu}SBS$ testing with a crosshead speed of 1 mm/minute. The mean ${\mu}SBS$ (n=20 for each group) was statistically compared using two-way ANOVA, Tukey HSD, and t test at 95% level. Also the interface of enamel and composite was evaluated under FE-SEM. The results of this study were as follows ; 1. The ${\mu}SBS$ of the SE Bond group to the enamel was significantly higher than that of the AdheSE group, the Tyrian group, and the Contax group in both the light-cured and the chemically cured composite resin (p < 0.05). 2. There was not a significant difference among the hdheSE group, the Tyrian group, and the Contax group in both the light-cured and the chemically cured composite resin. 3. The ${\mu}SBS$ of the light-cured composite resin was significantly higher than that of the chemically cured composite resin when same adhesive was applied to the enamel (p < 0.05). 4. The interface of enamel and all 2-step self-etch adhesives showed close adaptation, and so the incompatibility of the chemically cured composite resin did not show.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1985-1999
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• 2021

The purpose of this study is to apply urban heat island reduction techniques(green roof, cool roof, and cool pavements using heat insulation paint or blocks) recommended by the Environmental Protection Agency (EPA) to our study area and determine their actual effects through a comparative analysis between land cover objects. To this end, the area of Mugye-ri, Jangyu-myeon, Gimhae, Gyeongsangnam-do was selected as a study area, and measurements were taken using a drone DJI Matrice 300 RTK, which was equipped with a thermal infrared sensor FLIR Vue Pro R and a visible spectrum sensor H20T 1/2.3" CMOS, 12 MP. A total of nine heat maps, land cover objects (711) as a control group, and heat island reduction technique-applied land covering objects (180) were extracted every 1 hour and 30 minutes from 7:15 am to 7:15 pm on July 27. After calculating the effect values for each of the 180 objects extracted, the effects of each technique were integrated. Through the analysis based on daytime hours, the effect of reducing heat islands was found to be 4.71℃ for cool roof; 3.40℃ for green roof; and 0.43℃ and -0.85℃ for cool pavements using heat insulation paint and blocks, respectively. Comparing the effect by time period, it was found that the heat island reduction effect of the techniques was highest at 13:00, which is near the culmination hour, on the imaging date. Between 13:00 and 14:30, the efficiency of temperature reduction changed, with -8.19℃ for cool roof, -5.56℃ for green roof, and -1.78℃ and -1.57℃ for cool pavements using heat insulation paint and blocks, respectively. This study was a case study that verified the effects of urban heat island reduction techniques through the use of high-resolution images taken with drones. In the future, it is considered that it will be possible to present case studies that directly utilize micro-satellites with high-precision spatial resolution.

• Journal of the Korean Applied Science and Technology
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• v.35 no.4
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• pp.1487-1495
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• 2018

In this study, we investigated collagen production and hyaluronic acid production effects for wrinkle improvement test on 50 kinds of land plants and 10 kinds of marine plants native to Jeju Island as a part of developing customized cosmetic materials. Collagen and hyaluronic acid are recognized as major factors affecting skin aging. Cerastium holosteoides var. hallaisanense Mizushima extract ($100{\mu}g/mL$) produced more than 190% of collagen in the extracts of 50 kinds of land plants. Vicia angustifolia var. segetilis K. Koch. extract ($100{\mu}g/mL$) produced more than 160% of collagen. Ftsia japonica Decne. et Planch. extract ($100{\mu}g/mL$), Euonymus japonica Thunb. extract ($100{\mu}g/mL$), Suaeda malacosperma H.Hara extract ($100{\mu}g/mL$), Elaeagnus umbelellata Thunb. extract ($100{\mu}g/mL$), Sedum oryzifolium Makino extract ($100{\mu}g/mL$), Vicia unijuga A. Br. extract ($100{\mu}g/mL$), and Brassica juncea var. integrifolia Sinsk. extract ($100{\mu}g/mL$) showed more than 140% collagen production effect. Among the 10 species of marine plants, Sargassum macrocarpum C. Agardh extract ($50{\mu}g/mL$) produced more than 190% of collagen, and Carpopeltis angusta (Harvey) Okamura extract ($100{\mu}g/mL$), Codiumcoactum Okamura extract ($100{\mu}g/mL$), and Codium tenuifolium S. Shimada, T. Tadano & J. Tanaka extract ($100{\mu}g/mL$) showed more than 140% collagen production. Suaeda malacosperma H.Hara extract ($100{\mu}g/mL$) showed the effect of producing hyaluronic acid more than 140%, and Ftsia japonica Decne. et Planch. extract ($20{\mu}g/mL$) and Wistaria floribunda A.P. DC extract ($100{\mu}g/mL$) showed more than 130% hyalunonic acid production effect. Among the 10 species of marine plants, Peyssonnelia capensis Montagne extract ($100{\mu}g/mL$) was the most effective. Carpopeltis angusta (Harvey) Okamura extract ($100{\mu}g/mL$), Codiumcoactum Okamura extract ($100{\mu}g/mL$), and Codium tenuifolium S. Shimada, T. Tadano & J. Tanaka extract ($100{\mu}g/mL$) showed more than 120% hyalunonic acid production. Jeju resources, which have good collagen and hyaluronic acid production, showed the potential to be applied to solve the skin troubles of customized cosmetics in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.376-383
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• 2019

This study was conducted to investigate academic self-efficacy, critical thinking disposition, and learning motivation, which influence problem solving ability in nursing students. Data were collected from June 4, 2018 to June 29, 2018, and the final 213 data points were used for analysis. The SPSS/WIN 22.0 program was used to conduct descriptive statics, Pearson's correlation coefficient, t-tests, ANOVA, Scheffe's test and multiple regression analysis. The problem solving ability according to the general characteristics differed among residential type (F=3.930, p=0.021) and satisfaction with major (F=4.618, p=0.011). In the correlation between academic self-efficacy, critical thinking disposition, learning motivation, and problem solving ability of the subject, academic self-efficacy (r=0.573, p<0.001), critical thinking disposition (r=0.620, p<0.001), and learning motivation (r=0.563, p<0.001). The factors affecting the problem solving ability of the study subjects were major satisfaction (${\beta}=.117$, p=0.036), academic self-efficacy (${\beta}=0.314$, p<0.001), critical thinking disposition (${\beta}=0.318$, p<0.001), and learning motivation (${\beta}=0.217$, p=0.004), with an explanatory power of 45.2%. In this study, it is necessary to confirm the effects of the development of the intervention program and the application of the program, which can improve the problem solving ability of nursing students.

• Journal of the Korean Applied Science and Technology
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• v.38 no.1
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• pp.318-331
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• 2021

This study was conducted on the assumption that bone age would be more effective when it comes to physique and physical fitness assessment for adolescents, and the purpose of this study was to identify the differences in physique and physical fitness for students in their adolescence through bone age and chronological age in order to contribute to the well-balanced physique and physical fitness development in adolescents and the health improvement in students. Total 874 adolescents(483 males, 391 females) aged 11~16 were selected as subjects out of the total population of 1100 adolescents aged 6~16 based on the PAPS(Physical Activity Promotion System) and age standards of the TW3 method; and skeletal maturation, which symbolize the indicators of biological maturation, were evaluated by using the TW3(Tanner-Whitehouse 3) method after hand-wrist radiographs, and birth date was used for chronological age. A stadiometer and InBody 270 (Biospace, Korea) were used to measure 2 components in physique. A total of 7 components in physical fitness, which included muscular strength, muscular endurance, flexibility, power, cardiovascular endurance, balance, agility, were measured as well. A independent samples t-test was conducted for data processing using SPSS 25.0, and the significance level was set at p< .05. The study results are as follows. First, bone age and chronological age used for physique comparison in males aged 11 and 12, height and weight showed significant difference; in males aged 13, weight showed signicant difference. Weight and height in females aged 11, and height in females aged 12 showed significant difference. Second, bone age and chronological age used for physical fitness comparison in males aged 11, muscular strength, power, flexibility, cardiovascular endurance showed significant difference; in males aged 12, muscular strength. power, cardiovascular endurance; in males aged 13, flexibility showed significant difference. Muscular strength, power, flexibility, muscular endurance, cardiovascular endurance in females aged 11, and flexibility in females aged 14 showed significant difference. As a result, this study concluded that in a period of rapid skeletal growth, evaluating physique and physical fitness based on bone age is more accurate than evaluating based on chronological age.

• Korean Chemical Engineering Research
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• v.60 no.4
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• pp.535-543
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• 2022

A CPFD (Computational particle fluid dynamics) model of solar fluidized bed receiver of silicon carbide (SiC: average d_p=123 ㎛) particles was established, and the model was verified by comparing the simulation and experimental results to analyze the effect of particle behavior on the performance of the receiver. The relationship between the heat-absorbing performance and the particles behavior in the receiver was analyzed by simulating their behavior near bed surface, which is difficult to access experimentally. The CPFD simulation results showed good agreement with the experimental values on the solids holdup and its standard deviation under experimental condition in bed and freeboard regions. The local solid holdups near the bed surface, where particles primarily absorb solar heat energy and transfer it to the inside of the bed, showed a non-uniform distribution with a relatively low value at the center related with the bubble behavior in the bed. The local solid holdup increased the axial and radial non-uniformity in the freeboard region with the gas velocity, which explains well that the increase in the RSD (Relative standard deviation) of pressure drop across the freeboard region is responsible for the loss of solar energy reflected by the entrained particles in the particle receiver. The simulation results of local gas and particle velocities with gas velocity confirmed that the local particle behavior in the fluidized bed are closely related to the bubble behavior characterized by the properties of the Geldart B particles. The temperature difference of the fluidizing gas passing through the receiver per irradiance (∆T/I_DNI) was highly correlated with the RSD of the pressure drop across the bed surface and the freeboard regions. The CPFD simulation results can be used to improve the performance of the particle receiver through local particle behavior analysis.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.173-181
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• 2021

Chinese people have increasingly high interests in skin care and trust and prefer medical institution products and equipment to treat skin problems. The purpose of this study is to examine skin types and skin care situations, skin care recognition, and skin care satisfaction by gender in medical skin care center patients from in their 10s to 50s in Hainan Province, China. The questionnaire survey consisted of general characteristics(n=8), skin care situations(n=6), skin care recognition(n=11), and skin care satisfaction(n=21). A total of 328 questionnaires were researched from December 21, 2020 to January 9, 2021 using WeChat and Wenjuanxing program. Data were analyzed by SPSSWIN 21.0. Frequency analysis was applied for general characteristics, skin care situations, skin care recognition, and skin care satisfaction and Cronbach's α was used for the reliability of skin care recognition and satisfaction. The relationship among skin care situations, skin care recognition, and skin care satisfaction was analyzed by χ² test and t-test. As a result, the common skin types by gender was dry skin in females and oily skin in males. The highest skin trouble was melasma and pigments in females and pimple in males. The most common way to manage troubled skin was homecare in both females and males, followed by the dermatology department in females and pharmacy in males, suggesting a significant difference. The common period of skin trouble was from one year to three years and the most effective way to improve skin was good life habit, followed by laser treatment in both females and males. The most important consideration to choose a hospital was a famous franchise hospital and the most important matters in management was doctor or skin care professionalism. Skin care and treatment recognition was high in external effects for females and internal effects for males. Skin care satisfaction was high in service for females and effect for males. Skin care satisfaction was significantly higher in males than in females. In conclusion, there was a difference in skin types, skin troubles, skin problems, skin care ways, and skin care satisfaction by gender in Chinese medical skin care center patients. Therefore, this study suggests the development of various products and the need of systematic management programs.

• Journal of Life Science
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• v.32 no.12
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• pp.947-955
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• 2022

Silkworms, which have recently shown promise as functional health foods, show functional differences between varieties; therefore, the need for variety identification is emerging. In this study, we analyzed the whole silkworm genome to identify 10 unique silkworm varieties (Baekhwang, Baekok, Daebaek, Daebak, Daehwang, Goldensilk, Hansaeng, Joohwang, Kumkang, and Kumok) using single nucleotide polymorphisms (SNP) present in the genome as biomarkers. In addition, nine SNPs were selected to discriminate between varieties by selecting SNPs specific to each variety. We subsequently created a decision tree capable of cross-verifying each variety and classifying the varieties through sequential analysis. Restriction fragment length polymorphism (RFLP) was used for SNP867 and SNP9183 to differentiate between the varieties of Daehwang and Goldensilk and between Kumkang and Daebak, respectively. A tetra-primer amplification refractory (T-ARMS) mutation was used to analyze the remaining SNPs. As a result, we could isolate the same group or select an individual variety using the nine unique SNPs from SNP780 to SNP9183. Furthermore, nucleotide sequence analysis for the region confirmed that the alleles were identical. In conclusion, our results show that combining SNP analysis of the whole silkworm genome with the decision tree is of high value as a discriminative marker for classifying silkworm varieties.